With the prospect of global climate change, energy consumption and corresponding carbon dioxide emission are of particular importance in all areas of technology. A relatively large proportion of primary energy is used for the operation of residential, commercial and industrial buildings, for example for lighting and heating, ventilation and air conditioning (HVAC). It is widely recognized that energy consumption in this area is one of the biggest carbon dioxide emission driver nowadays. In order to reduce carbon dioxide emissions worldwide, apart from utilizing more clean energy sources, energy efficiency in the building sector is thus one of the best opportunities to address the problems of global climate change. This is particularly true in view of growing populations, the widespread use of electricity driven technologies and an increase in the amount of man-made buildings on the planet.
One precondition for achieving high energy efficiency is an in-depth understanding and prediction of energy consumption. In the prior art, some approaches for predicting an energy consumption of a given appliance or site exists. Such approaches are based on analyzing historical consumption data of utility meters by applying a predictive algorithm, for example based on statistical data regression or neural networks. However, based on relatively limited data sets, for example a monthly total energy consumption of a building, and very complex energy consumption patterns based on a plurality of input parameters, such methods often do not provide sufficiently precise prediction results or require very long training times. Moreover, such methods are often based on a retrospective analysis of monthly consumption data and therefore do not allow an owner or operator of a building to assess the energy efficiency of one or several buildings in real-time.
Accordingly, there is a need for better systems and methods for predicting an energy consumption of one or more buildings. Preferably, such improved systems and methods should allow a site manager or owner to monitor and compare a predicted energy consumption with a true energy consumption of the building in real-time.